It can safely be said that businesses and financial institutions have never before had the level of access to data they have today. This is a fact that likely sends a tremor of excitement through many who recognize the potential to turn data into growth and for financial institutions specifically to increase margins. But there’s a complication. A few of them, actually.
Data—heaps and heaps of it—isn’t something a human mind can organize and parse. And honestly, on its own, data doesn’t exactly tell us anything. Instead, it becomes valuable when one has the tools to draw insight out of the information and the relationships that the data represents. It takes technology to do that, in a process referred to as analytics. This, too, opens up a whole different set of problems, such as determining what data to look at and choosing which data should be correlated to help grow business.
Start at the Beginning
Before any institution can draw value from their data, they need to understand three distinct types of analytics that can lead us down the problem-solution path. They are:
- Diagnostic: analytics that tell us why something happened/is happening
- Predictive: analytics that tell us what is likely to happen next
- Prescriptive: analytics that help us find solutions to problems
From this understanding, financial institutions can move forward with a goal of choosing the outcomes they’d like to focus on and setting up the analytics so they can deliver diagnostic, predictive and prescriptive tools correlated to these outcomes.
Depending on the needs of your institution, there is an almost endless number of diagnostic, predictive and prescriptive analytics you can review and leverage to improve lending margins. When talking to clients, we have noticed four major themes that come up that we’d suggest focusing on, at least at first. These include increasing wallet share, improving and increasing automation, refining underwriting and steering institutional growth.
A Closer Look at 4 Outcomes
The average institution has just 25% of its customers’ wallet share. Since getting a new customer can be anywhere from five to 25 times more expensive than retaining one you already have, it makes sense that increasing wallet share would be a prime objective for most financial institutions. Through analytics, you can get diagnostic information about why and at what point existing customers abandon applications for new products and what system bottlenecks are slowing down their experience. This is especially important since studies show us that as many as 40 percent of customers abandon the new account opening process because it’s too lengthy. Prescriptive analytics can help you find the optimum route for data entry and transfer so that you can lower abandonment rates and speed up the customer experience.
Reducing expenses is one way to increase margins, and automation is a tool that can quickly do that. To increase automation, you must first get diagnostic information by running analytics on your structured data to explore overrides, which can be automated. From there you can get prescriptive analytics to determine how to tune your decisioning model and increase your automated decisioning capabilities, adjust rules and allow for more auto-approvals. Increased automated decisioning can also help increase cross-sell opportunities, which doesn’t just lower cost but also increases wallet share.
Let’s not allow predictive analytics to get lost in the shuffle here. Using a champion challenger comparison to measure the decisioning outcomes of loan origination software (LOS) against the performance of your loan portfolio can give some predictive and prescriptive analytics that guide you in adjusting your underwriting criteria.
Another example of predictive analytics is using data to analyze decision rules to see what changes can increase growth. The potential risks of these changes can also be measured and weighed against the projected growth to determine whether the path forward is within the institution’s tolerance.
Getting information is easy. Organizing and leveraging that information is critical. That takes more than intention; it takes the right tools and the foresight to use them properly.
